Class-based collocations for Word Sense Disambiguation

Class-based Collocations for Word-Sense Disambiguation Tom O’Hara Department of Computer Science New Mexico State University Las Cruces, NM 88003-8001 tomohara@cs.nmsu.edu Rebecca Bruce Department of Computer Science University of North Carolina at Asheville Asheville, NC 28804-3299 bruce@cs.unca.edu Jeff Donner Department of Computer Science New Mexico State University Las Cruces, NM 88003-8001 jdonner@cs.nmsu.edu Janyce Wiebe Department of Computer Science University of Pittsburgh Pittsburgh, PA 15260-4034 wiebe@cs.pitt.edu Abstract This paper describes the NMSU-Pitt-UNCA word-sense disambiguation system participat- ing in the Senseval-3 English lexical sample task. The focus of the work is on using seman- tic class-based collocations to augment tradi- tional word-based collocations. Three separate sources of word relatedness are used for these collocations: 1) WordNet hypernym relations; 2) cluster-based word similarity classes; and 3) dictionary definition analysis. 1 Introduction Supervised systems for word-sense disambigua- tion (WSD) often rely upon word collocations (i.e., sense-specific keywords) to provide clues on the most likely sense for a word given the context. In the second Senseval competition, these features figured predominantly among the feature sets for the leading systems (Mihalcea, 2002; Yarowsky et al., 2001; Seo et al., 2001). A limitation of such features is that the words selected must occur in the test data in order for the features to apply. To alleviate this problem, class-based approaches augment word-level fea- tures with category-level ones (Ide and V´eronis, 1998; Jurafsky and Martin, 2000). When ap- plied to collocational features, this approach ef- fectively uses class labels rather than wordforms in deriving the collocational features. This research focuses on the determination of class-based collocations to improve word- sense disambiguation. We do not address refine- ment of existing algorithms for machine learn- ing. Therefore, a commonly used decision tree algorithm is employed to combine the various features when performing classification. This paper describes the NMSU-Pitt- UNCA system we developed for the third Senseval competition. Section 2 presents an overview of the feature set used in the system. Section 3 describes how the class-based colloca- tions are derived. Section 4 shows the results over the Senseval-3 data and includes detailed analysis of the performance of the various col- locational features. 2 System Overview We use a decision tree algorithm for word-sense disambiguation that combines features from the local context of the target word with other lex- ical features representing the broader context. Figure 1 presents the features that are used in this application. In the first Senseval com- petition, we used the first two groups of fea- tures, Local-context features and Collocational features, with competitive results (O’Hara et al., 2000). Five of the local-context features represent the part of speech (POS) of words immediately surrounding the target word. These five fea- tures are POS±i for i from -2 to +2), where POS+1, for example, represents the POS of the word immediately following the target word. Five other local-context features represent the word tokens immediately surrounding the target word (Word±i for i from −2 to +2). Each Word±i feature is multi-valued; its values correspond to all possible word tokens. There …

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